Cancer Imaging最新文献

{"title":"Identification of HER2-over-expression, HER2-low-expression, and HER2-zero-expression statuses in breast cancer based on ¹⁸F-FDG PET/CT radiomics.","authors":"Xuefeng Hou, Kun Chen, Huiwen Luo, Wengui Xu, Xiaofeng Li","doi":"10.1186/s40644-025-00880-2","DOIUrl":"10.1186/s40644-025-00880-2","url":null,"abstract":"<p><strong>Purpose: </strong>According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the following three groups: HER2-over-expression, HER2-low-expression, and HER2-zero-expression. HER2-negative expression was reclassified into HER2-low-expression and HER2-zero-expression. This study aimed to identify three different HER2 expression statuses for breast cancer (BC) patients using PET/CT radiomics and clinicopathological characteristics.</p><p><strong>Methods and materials: </strong>A total of 315 BC patients who met the inclusion and exclusion criteria from two institutions were retrospectively included. The patients in institution 1 were divided into the training set and the independent validation set according to the ratio of 7:3, and institution 2 was used as the external validation set. According to the results of pathological examination, all BC patients were divided into HER2-over-expression, HER2-low-expression, and HER2-zero-expression. First, PET/CT radiomic features and clinicopathological features based on each patient were extracted and collected. Second, multiple methods were used to perform feature screening and feature selection. Then, four machine learning classifiers, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), were constructed to identify HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others. The receiver operator characteristic (ROC) curve was plotted to measure the model's predictive power.</p><p><strong>Results: </strong>According to the feature screening process, 8, 10, and 2 radiomics features and 2 clinicopathological features were finally selected to construct three prediction models (HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others). For HER2-over-expression vs. others, the RF model outperformed other models with an AUC value of 0.843 (95%CI: 0.774-0.897), 0.785 (95%CI: 0.665-0.877), and 0.788 (95%CI: 0.708-0.868) in the training set, independent validation set, and external validation set. Concerning HER2-low-expression vs. others, the outperformance of the LR model over other models was identified with an AUC value of 0.783 (95%CI: 0.708-0.846), 0.756 (95%CI: 0.634-0.854), and 0.779 (95%CI: 0.698-0.860) in the training set, independent validation set, and external validation set. Whereas, the KNN model was confirmed as the optimal model to distinguish HER2-zero-expression from others, with an AUC value of 0.929 (95%CI: 0.890-0.958), 0.847 (95%CI: 0.764-0.910), and 0.835 (95%CI: 0.762-0.908) in the training set, independent validation set, and external validation set.</p><p><strong>Conclusion: </strong>Combined PET/CT radiomic models integrating with clinicopathological characteristics are non-invasively predictive of different HER2 statuses of BC patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"62"},"PeriodicalIF":3.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.","authors":"Yuqi Yan, Yuanzhen Liu, Yao Wang, Tian Jiang, Jiayu Xie, Yahan Zhou, Xin Liu, Meiying Yan, Qiuqing Zheng, Haifei Xu, Jinxiao Chen, Lin Sui, Chen Chen, RongRong Ru, Kai Wang, Anli Zhao, Shiyan Li, Ying Zhu, Yang Zhang, Vicky Yang Wang, Dong Xu","doi":"10.1186/s40644-025-00879-9","DOIUrl":"https://doi.org/10.1186/s40644-025-00879-9","url":null,"abstract":"<p><strong>Objective: </strong>Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical Diagnosis Model (PTs-HDM) for preoperative identification and grading.</p><p><strong>Methods: </strong>Ultrasound images from five hospitals were retrospectively collected, with all patients having undergone surgical pathological confirmation of either PTs or fibroadenomas (FAs). PTs-HDM follows a two-stage classification: first distinguishing PTs from FAs, then grading PTs into benign or borderline/malignant. Model performance metrics including AUC and accuracy were quantitatively evaluated. A comparative analysis was conducted between the algorithm's diagnostic capabilities and those of radiologists with varying clinical experience within an external validation cohort. Through the provision of PTs-HDM's automated classification outputs and associated thermal activation mapping guidance, we systematically assessed the enhancement in radiologists' diagnostic concordance and classification accuracy.</p><p><strong>Results: </strong>A total of 712 patients were included. On the external test set, PTs-HDM achieved an AUC of 0.883, accuracy of 87.3% for PT vs. FA classification. Subgroup analysis showed high accuracy for tumors < 2 cm (90.9%). In hierarchical classification, the model obtained an AUC of 0.856 and accuracy of 80.9%. Radiologists' performance improved with PTs-HDM assistance, with binary classification accuracy increasing from 82.7%, 67.7%, and 64.2-87.6%, 76.6%, and 82.1% for senior, attending, and resident radiologists, respectively. Their hierarchical classification AUCs improved from 0.566 to 0.827 to 0.725-0.837. PTs-HDM also enhanced inter-radiologist consistency, increasing Kappa values from - 0.05 to 0.41 to 0.12 to 0.65, and the intraclass correlation coefficient from 0.19 to 0.45.</p><p><strong>Conclusion: </strong>PTs-HDM shows strong diagnostic performance, especially for small lesions, and improves radiologists' accuracy across all experience levels, bridging diagnostic gaps and providing reliable support for PTs' hierarchical diagnosis.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"61"},"PeriodicalIF":3.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of a simple positioning aid device on the diagnostic performance of thyroid cancer in CT scans: a randomized controlled trial.","authors":"Wei-Hua Lin, Hui-Juan Huang, Wen-Cong Yang, Qing-Wen Huang, Rui-Gang Huang, Fu-Rong Luo, Dong-Yi Chen, Zheng-Han Yang, Hai-Tao Li, Hui-Huang Zeng, Hui-Jun Xiao","doi":"10.1186/s40644-025-00878-w","DOIUrl":"https://doi.org/10.1186/s40644-025-00878-w","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the effectiveness of a simple positioning aid device in neck CT scans for the diagnosis of thyroid cancer, with a focus on its influence on image quality and diagnostic accuracy.</p><p><strong>Methods: </strong>A randomized controlled trial was conducted involving 180 patients with suspected thyroid cancer. Participants were randomly assigned to two groups: the device-assisted positioning group (Group A) and the traditional positioning group (Group B). A total of 147 patients who underwent enhanced neck CT scans and subsequent surgical pathological biopsies were included in the final analysis. Image quality and thyroid disease diagnoses were independently assessed by two experienced radiologists, with a unified consensus for the final conclusions. Objective imaging parameters and subjective ratings were used to evaluate image quality. Pathological findings served as the gold standard to compare the diagnostic accuracy of the two groups for thyroid malignancy, capsular invasion, and lymph node metastasis. Additionally, radiation doses in both groups were compared.</p><p><strong>Results: </strong>A total of 147 patients were included in the analysis, with 72 patients in Group A and 75 in Group B. The baseline characteristics of the two groups were similar (P > 0.05). Group A demonstrated significantly superior image quality compared to Group B, with shorter length of artifacts (LA), lower proportion of affected thyroid (PA), and lower artifact index (AI). Subjective assessments also favored Group A, showing better ratings for regional artifacts and overall image quality. In terms of diagnostic accuracy, Group A outperformed Group B in detecting thyroid cancer (AUC: 0.852 vs. 0.676, P = 0.021). For the right thyroid lobe, Group A had significantly better diagnostic performance (AUC: 0.897 vs. 0.746, P = 0.016). Group A also showed superior performance in diagnosing capsular invasion (AUC: 0.861 vs. 0.721, P = 0.037), with similar results observed for both the left and right thyroid lobes. There was no significant difference between the groups in diagnosing lymph node metastasis. Furthermore, thyroid region radiation doses (CTDIvol and SSDE) were significantly lower in Group A compared to Group B.</p><p><strong>Conclusion: </strong>The use of a positioning aid device significantly improves CT image quality, enhancing diagnostic accuracy for malignant thyroid lesions and capsular invasion, while also reducing radiation exposure.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"60"},"PeriodicalIF":3.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study.","authors":"Zhihui Chen, Hongqing Zhu, Hongmin Shu, Jianbo Zhang, Kangchen Gu, Wenjun Yao","doi":"10.1186/s40644-025-00875-z","DOIUrl":"https://doi.org/10.1186/s40644-025-00875-z","url":null,"abstract":"<p><strong>Objectives: </strong>The World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) is crucial for prognosis and treatment planning. This study aims to predict the grade using intratumoral and peritumoral subregional CT radiomics analysis for better clinical interventions.</p><p><strong>Methods: </strong>Data from two hospitals included 513 ccRCC patients, who were divided into training (70%), validation (30%), and an external validation set (testing) of 67 patients. Using ITK-SNAP, two radiologists annotated tumor regions of interest (ROI) and extended surrounding areas by 1 mm, 3 mm, and 5 mm. The K-means clustering algorithm divided the tumor region into three sub-regions, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression identified the most predictive features. Various machine learning models were established, including radiomics models, peritumoral radiomics models, models based on intratumoral heterogeneity (ITH) score, clinical models, and comprehensive models. Predictive ability was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, DeLong tests, calibration curves, and decision curves.</p><p><strong>Results: </strong>The combined model showed strong predictive power with an AUC of 0.852 (95% CI: 0.725-0.979) on the test data, outperforming individual models. The ITH score model was highly precise, with AUCs of 0.891 (95% CI: 0.854-0.927) in training, 0.877 (95% CI: 0.814-0.941) in validation, and 0.847 (95% CI: 0.725-0.969) in testing, proving its superior predictive ability across datasets.</p><p><strong>Conclusion: </strong>A comprehensive model combining Habitat, Peri1mm, and salient clinical features was significantly more accurate in predicting ccRCC pathologic grading.</p><p><strong>Key points: </strong>Question: Characterize tumor heterogeneity to non-invasively predict WHO/ISUP pathological grading preoperatively.</p><p><strong>Findings: </strong>An integrated model combining subregion characterization, peritumoral characteristics, and clinical features can predict ccRCC grade preoperatively.</p><p><strong>Clinical relevance: </strong>Subregion tumor characterization outperforms the single-entity approach. The integrated model, compared with the radiomics model, boosts grading and prognostic accuracy for more targeted clinical actions.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"59"},"PeriodicalIF":3.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing ¹⁸F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm.","authors":"Zhihao Chen, Hongxing Yang, Ming Qi, Wen Chen, Fei Liu, Shaoli Song, Jianping Zhang","doi":"10.1186/s40644-025-00877-x","DOIUrl":"https://doi.org/10.1186/s40644-025-00877-x","url":null,"abstract":"<p><strong>Background: </strong>As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting lesion diagnostics. It is crucial to identify methods that ensure consistent diagnostic accuracy and maintain image quality. Deep progressive learning (DPL) algorithm, an Artificial Intelligence(AI)-based PET reconstruction technique, offers a promising solution.</p><p><strong>Methods: </strong>150 patients underwent ¹⁸F-FDG PET/CT scans and were categorized by BMI into underweight, normal, and overweight groups. PET images were reconstructed using both OSEM and DPL and their image quality was assessed both visually and quantitatively. Visual assessment employed a 5-point Likert scale to evaluate overall score, image sharpness, image noise, and diagnostic confidence. Quantitative assessment parameters included the background liver image-uniformity-index ([Formula: see text]) and signal-to-noise ratio ([Formula: see text]). Additionally, 466 identifiable lesions were categorized by size: sub-centimeter and larger. We compared maximum standard uptake value ([Formula: see text]), signal-to-background ratio ([Formula: see text]), [Formula: see text], contrast-to-background ratio ([Formula: see text]), and contrast-to-noise ratio ([Formula: see text]) of these lesions to evaluate the diagnostic performance of the DPL and OSEM algorithms across different lesion sizes and BMI categories.</p><p><strong>Results: </strong>DPL produced superior PET image quality compared to OSEM across all BMI groups. The visual quality of DPL showed a slight decline with increasing BMI, while OSEM exhibited a more significant decline. DPL maintained a stable [Formula: see text] across BMI increases, whereas OSEM exhibited increased noise. In the DPL group, quantitative image quality for overweight patients matched that of normal patients with minimal variance from underweight patients. In contrast, OSEM demonstrated significant declines in quantitative image quality with rising BMI. DPL yielded significantly higher contrast ([Formula: see text], [Formula: see text],[Formula: see text]) and [Formula: see text] than OSEM for all lesions across all BMI categories.</p><p><strong>Conclusion: </strong>DPL consistently provided superior image quality and lesion diagnostic performance compared to OSEM across all BMI categories in ¹⁸F-FDG PET/CT scans. Therefore, we recommend using the DPL algorithm for ¹⁸F-FDG PET/CT image reconstruction in all BMI patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"58"},"PeriodicalIF":3.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12044768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.","authors":"Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang","doi":"10.1186/s40644-025-00876-y","DOIUrl":"https://doi.org/10.1186/s40644-025-00876-y","url":null,"abstract":"<p><strong>Background: </strong>The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with metastasis in No. 253 lymph node (No. 253 LN) being a key determining factor. This study aimed to develop a machine learning model for predicting metastasis in No. 253 LN.</p><p><strong>Methods: </strong>We retrospectively collected clinical data from 2,118 patients with left-sided CRC and contrast-enhanced CT images from 310 of these patients. From this data, a test set, a training set, and a temporal validation set were constructed. Logistic regression models were used to develop a clinical model, a CT model, and a radiomics model, which were then integrated into a combined model using logical rules. Finally, these models were evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), precision-recall (PR) curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).</p><p><strong>Results: </strong>A clinical model, a CT model, and a radiomics model were constructed using univariate logistic regression. A combined model was developed by integrating the clinical, CT, and radiomics models, with positivity defined as all three models being positive at a 90% sensitivity threshold. The clinical model included six predictive factors: tumor site, endoscopic obstruction, CEA levels, growth type, differentiation grade, and pathological classification. The CT model utilized largest lymph node average CT value, short-axis diameter and long-axis diameter. The radiomics model incorporated maximum gray level intensity within the region of interest, large area high gray level emphasis, small area high gray level emphasis and surface area to volume ratio. In the test set, the AUCs for the clinical, CT, radiomics, and combined models were 0.694, 0.663, 0.72, and 0.663, respectively, while in the temporal validation set, they were 0.743, 0.629, 0.716, and 0.8. Specifically, the combined model demonstrated a sensitivity of 0.8 and a specificity of 0.8 in the temporal validation set. By comparing the PR and DCA curves, the combined model demonstrated better performance. Additionally, the combined model showed moderate improvements in INR and IDI compared to other models.</p><p><strong>Conclusion: </strong>A clinical and CT-based radiomics model shows promise in predicting No. 253 LN metastasis in left-sided CRC and provides insights for optimizing IMA ligation strategies.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"57"},"PeriodicalIF":3.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12039209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building a pre-surgical multiparametric-MRI-based morphologic, qualitative, semiquantitative, first and high-order radiomic predictive treatment response model for undifferentiated pleomorphic sarcoma to replace RECIST.","authors":"Raul F Valenzuela, Elvis Duran-Sierra, Mathew Antony, Behrang Amini, Sam Lo, Keila E Torres, Robert S Benjamin, Jingfei Ma, Ken-Pin Hwang, R Jason Stafford, Dejka Araujo, Andrew J Bishop, Ravin Ratan, Wei-Lien Wang, Jossue Espinoza, Pia V Valenzuela, Chengyue Wu, John E Madewell, William A Murphy, Colleen M Costelloe","doi":"10.1186/s40644-025-00873-1","DOIUrl":"https://doi.org/10.1186/s40644-025-00873-1","url":null,"abstract":"<p><strong>Background: </strong>Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft-tissue sarcomas. It demonstrates post-therapeutic hemosiderin deposition, granulation tissue formation, fibrosis, and calcification. Our research aims to establish the multiparametric MRI (mp-MRI) value for predicting UPS treatment response.</p><p><strong>Methods: </strong>An IRB-approved retrospective study included 33 extremity UPS patients with pre-operative mp-MRI, including diffusion-weighted imaging (DWI), contrast-enhanced susceptibility-weighted imaging (CE-SWI), and perfusion-weighted imaging with dynamic contrast-enhancement (PWI/DCE), and surgical resection between February 2021 and May 2023. Lesions were visually classified on CE-SWI into one of 6 morphology patterns. On PWI/DCE, lesions were classified into one of 6 patterns, and time-intensity curves (TICs) were classified as types I-V. Patients were categorized into three groups based on the percentage of pathology-assessed treatment effect (PATE) in the surgical specimen: Responders (> = 90% PATE, n = 16), partial-responders (31-89% PATE, n = 10), and non-responders (< = 30% PATE, n = 7).</p><p><strong>Results: </strong>At post-radiation therapy (PRT), a CE-SWI Complete-Ring pattern was observed in 71% of responders (p = 7.71 × 10^-6). On PWI/DCE images, 79% of responders displayed a Capsular pattern (p = 1.49 × 10^-7), and 100% demonstrated a TIC-type II (p = 8.32 × 10^-7). ROC analysis comparing responders (n = 14) vs. partial/non-responders (n = 16) at PRT showed that the model combining PWI/DCE TIC-type II, PWI/DCE Capsular pattern, and CE-SWI Complete-Ring pattern yielded the highest classification performance (AUC = 0.99), outperforming PWI/DCE Capsular + TIC-type II (AUC = 0.97), PWI/DCE Capsular (AUC = 0.89), PWI/DCE TIC-type II (AUC = 0.88), and CE-SWI Complete Ring (AUC = 0.79). Contrary to prior reports, DWI/ADC played a secondary role in predicting response: ADC mean & skewness (AUC = 0.63). RECIST demonstrated 100% stability at PRT and 100% pseudo-progression at PC in responders and partial/non-responders (AUC = 0.47).</p><p><strong>Conclusion: </strong>Mp-MRI-derived features are valuable in assessing UPS treatment response. A pre-operative model that combines PWI/DCE TIC-type II, PWI/DCE Capsular pattern, and CE-SWI Complete Ring pattern can reliably predict successfully treated UPS with > = 90% PATE, outperforming RECIST, which was proven unreliable in separating responders from partial/non-responders. Institutions that have not yet implemented CE-SWI can rely on a single-sequence approach based on PWI/DCE, combining the presence of TIC II and Capsular enhancement as criteria for response prediction.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"56"},"PeriodicalIF":3.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12032704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of 3D ARFI imaging of prostate cancer: diagnostic reliability and concordance with MpMRI.","authors":"Spencer R Moavenzadeh, Derek Y Chan, Eric S Adams, Sriram Deivasigamani, Srinath Kotamarti, Mark L Palmeri, Thomas J Polascik, Kathryn R Nightingale","doi":"10.1186/s40644-025-00874-0","DOIUrl":"https://doi.org/10.1186/s40644-025-00874-0","url":null,"abstract":"<p><strong>Purpose: </strong>The prevalence of prostate cancer (PCa) necessitates advanced diagnostic approaches for detection and lesion characterization. Utilizing two patient cohorts (n = 85), this study analyzes a custom-designed 3D ultrasonic acoustic radiation force impulse (ARFI) elasticity imaging system alongside an Index of Suspicion (IOS) lesion ranking system to evaluate reader sensitivity, positive predictive values, inter-reader reliability, and ARFI-mpMRI concordance. The IOS system provides standardized criteria for lesion assessment, enabling consistency in stratifying PCa lesion suspicion.</p><p><strong>Materials and methods: </strong>Three readers were trained on multiparametric ultrasound (mpUS) (combined ARFI and B-mode) prostate image volumes from 6 patients based on the IOS criteria. The readers then marked suspicious lesions in 79 patients who were retrospectively compared with histopathology-identified (Cohort I, post-radical prostatectomy) or biopsy-confirmed (Cohort II) cancerous regions.</p><p><strong>Results: </strong>The IOS criteria stratified lesions by Gleason grade (GG), with a higher IOS correlating with more aggressive lesions. mpUS imaging was more sensitive for detecting lesions with higher GG and preferentially identified lesions with lower MR apparent-diffusion coefficients and signs of extraprostatic extension. mpUS imaging demonstrated substantial inter-reader reliability and moderate overlap with mpMRI lesions, with increasing sensitivity to higher MRI PI-RADS score lesions. mpUS imaging was less sensitive than mpMRI to lesions with lower GG.</p><p><strong>Conclusions: </strong>The increased sensitivity of mpUS imaging to higher GG lesions and adverse histopathological factors, along with moderate agreement with mpMRI, suggest that mpUS has the potential to guide biopsy targeting of mpMRI-visible lesions or serve as an alternative biopsy-targeting approach when mpMRI is unavailable or clinically contraindicated.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"55"},"PeriodicalIF":3.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cabazitaxel-induced ureteritis in metastatic castration-resistant prostate cancer patients: a single center case series 2014-2024.","authors":"Ahmed H Zedan, Jesper S Gade, Karsten Egbert Arnold Zieger, Mads H Poulsen, Anja Schmidt Vejlgaard, Filip Lund Hjorth Fredensborg","doi":"10.1186/s40644-025-00872-2","DOIUrl":"https://doi.org/10.1186/s40644-025-00872-2","url":null,"abstract":"<p><strong>Background: </strong>One of the main and effective therapy choices for patients with metastatic castration-resistant prostate cancer (mCRPC) is cabazitaxel (CBZ). Cystitis and hematuria are among the most significant non-hematological adverse events associated with CBZ treatment. But because the prevalence of CBZ-induced ureteritis has not been thoroughly studied, this case series investigation was carried out to emphasize the condition's clinical relevance and potential treatment alternatives.</p><p><strong>Case presentation: </strong>Between June 2014 and May 2024, 354 patients diagnosed with mCRPC were treated with CBZ at the Department of Oncology, Vejle Hospital. A total of 36 patients (10%) exhibited ureteritis-like symptoms, presenting with discomfort in the pelvis, lower abdomen, or flanks, with or without hematuria. Radiological evidence of ureter changes was present in 29 out of 36 individuals (80%), along with hydronephrosis/hydroureter in some patients. Prior to therapy with CBZ, radiation to the pelvis or lower abdomen was documented in 7 out of 36 patients (19%). Various analgesics and dosage modifications were considered for the therapy of CBZ-induced ureteritis, with treatment discontinuation yielding the most favorable results.</p><p><strong>Conclusion: </strong>The onset of ureteritis during CBZ treatment is an underrated side effect in clinical practice. Hematuria and hydronephrosis/hydroureter are the most associated complications. Both analgesics and dosage reduction should be contemplated for management, while therapy cessation may be requisite in certain individuals.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"54"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of CE-MATRIX-Enhanced FLAIR imaging in the detection of leptomeningeal metastasis.","authors":"Junhui Yuan, Shaobo Fang, Fan Meng, Yue Wu, Dongqiu Shan, Chunmiao Xu, Renzhi Zhang, Xuejun Chen","doi":"10.1186/s40644-025-00867-z","DOIUrl":"10.1186/s40644-025-00867-z","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the diagnostic value of CE-MATRIX-T1FLAIR and 3D CE-T2FLAIR sequences based on Contrast Enhancement Modulated flip Angle Technique in Refocused Imaging with eXtended echo train (CE-MATRIX) technology for detecting Leptomeningeal Metastasis (LM) using Fluid Attenuated Inversion Recovery (FLAIR) imaging.</p><p><strong>Methods: </strong>This prospective study included 563 hospitalized patients with clinically suspected LM, diagnosed with malignant tumors between January 2022 and October 2023 at Henan Cancer Hospital. Both CE-MATRIX-T1FLAIR and 3D CE-T2FLAIR sequences were used for imaging. Two radiologists independently evaluated image quality, diagnostic confidence, and objective measurements, diagnosing LM as positive or negative, with disagreements resolved by consultation. Subjective and objective scores were compared using the Wilcoxon signed-rank test. The diagnostic performance of the sequences was compared using ROC curve analysis, with cerebrospinal fluid (CSF) cytology as the gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) values were calculated and compared using Z-tests.</p><p><strong>Results: </strong>LM was confirmed in 321 patients. CE-MATRIX-T1FLAIR showed superior subjective scores in image quality and diagnostic confidence (p < 0.001). Though CE-MATRIX-T1FLAIR had a lower SNR (p = 0.013), it demonstrated higher sensitivity, specificity, PPV, NPV, accuracy, and AUC than 3D CE-T2FLAIR (p < 0.001). Both sequences provided effective diagnosis and differentiation of LM.</p><p><strong>Conclusion: </strong>CE-MATRIX-T1FLAIR offers superior diagnostic performance compared to 3D CE-T2FLAIR for LM, with slightly better subjective ratings despite a lower SNR. Both sequences are effective for diagnosing LM.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"53"},"PeriodicalIF":3.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11980099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}